{"id":"W4248260714","doi":"10.1145/376284.375693","title":"SPARTAN","year":2001,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Spartan; Lossy compression; Data mining; Table (database); Data compression; Data structure; Theoretical computer science; Algorithm; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001762724,0.0001029215,0.0001160101,0.00005163406,0.0001066076,0.00006988255,0.001276638,0.0000674635,0.00008203275],"category_scores_gemma":[0.0001043154,0.00009286328,0.00004548988,0.000269614,0.0000213831,0.0002876374,0.0002972975,0.0001635586,0.0004712899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001967736,"about_ca_system_score_gemma":0.00004361837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006928029,"about_ca_topic_score_gemma":0.00002805779,"domain_scores_codex":[0.9990415,0.00004517238,0.0001648735,0.0003110294,0.0001447644,0.0002926524],"domain_scores_gemma":[0.9987539,0.0001012301,0.00004703638,0.0009430373,0.00005570396,0.00009911215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008854988,0.00005145661,0.01113924,0.00000531589,0.00001490622,0.00006034387,0.0003688881,0.00004624093,0.001035408,0.04930266,0.01077481,0.9271919],"study_design_scores_gemma":[0.0009293414,0.0004714862,0.00945825,0.0001011335,0.00001764105,0.0001514659,0.00007267034,0.2279545,0.001587793,0.4847562,0.2732982,0.001201307],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08980364,0.00006780626,0.8905946,0.002104115,0.0006092936,0.00005947297,3.030451e-7,0.0002878392,0.01647292],"genre_scores_gemma":[0.9162623,0.00008735772,0.08013705,0.0008385464,0.0001701549,0.000008686688,8.615266e-7,0.000008049597,0.002486972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9259906,"threshold_uncertainty_score":0.6057635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04318286130473845,"score_gpt":0.272622653250019,"score_spread":0.2294397919452806,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}